RFTSystems/RFT_Omega_API

1Rendered Frame Theory — Stabilising System Verification Panel2 3Interactive verification panel for Rendered Frame Theory (RFT) harmonic stability under controlled synthetic noise.4This Space is a reproducible test harness for anticipatory stability (QΩ) and synchronisation coherence (ζ_sync) across multiple domains.5	•	Domains: AI/Neural, SpaceX/Aerospace, Energy/RHES, Extreme Perturbation6	•	Noise control: slider for σ (0.00–0.30) to probe robustness7	•	Outputs: JSON with mean QΩ / ζ_sync, status classification, and timestamp8	•	Logging: Save Run Log downloads a timestamped .json record for audit trails9	•	Reference DOI: https://doi.org/10.5281/zenodo.1746672210 11Live panel: https://rftsystems-rft-omega-api.hf.space12 13⸻14 15How to Use16	1.	Open the panel → https://rftsystems-rft-omega-api.hf.space17	2.	Select a System Profile (AI/Neural, SpaceX/Aerospace, Energy/RHES, Extreme Perturbation).18	3.	Choose Noise Distribution (gauss or uniform).19	4.	Adjust Synthetic Noise (σ) with the slider (0.00–0.30).20	5.	Click Run Simulation → JSON output appears with live QΩ/ζ_sync and status.21	6.	Click 💾 Save Run Log to download the result as a .json (timestamped).22 23    Example output24    {25  "profile": "AI / Neural",26  "noise_scale": 0.080,27  "distribution": "gauss",28  "QΩ_mean": 0.834,29  "ζ_sync_mean": 0.799,30  "status_majority": "perturbed",31  "timestamp_utc": "2025-10-29T14:04:05.114382Z",32  "rft_notice": "All Rights Reserved — RFT-IPURL v1.0 (UK / Berne). Research validation use only. No reverse-engineering without written consent."33}34What to Expect35 36Typical stable ranges (nominal conditions)37Metric38Range39Meaning40QΩ410.82–0.8942Harmonic stability factor (amplitude)43ζ_sync440.75–0.8845Synchronisation coherence (phase)46Status classification (qualitative)47	•	nominal — low variance; coherent equilibrium48	•	perturbed — moderate variance; coherent but stressed49	•	critical — high variance; edge-of-instability50 51Noise guidance by profile (starting points)52	•	AI / Neural: σ ≈ 0.01–0.10 (training drift / GPU jitter)53	•	SpaceX / Aerospace: σ ≈ 0.03–0.12 (vibration / telemetry lag)54	•	Energy / RHES: σ ≈ 0.02–0.10 (grid oscillations / load steps)55	•	Extreme Perturbation: σ up to 0.30 (stress testing / failure modes)56 57Notes58	•	The panel applies domain-specific weighting (relative importance of QΩ vs ζ_sync).59	•	Outputs are bounded to [0.00, 0.99] to prevent saturation artifacts and maintain comparability.60	•	Repeated runs at fixed σ typically show < 0.05 variance in stable regimes.61 62⸻63 64Validation Purpose65	•	Benchmark harmonic resilience under controlled perturbations (σ sweeps).66	•	Study predictive drift signals: observe divergence/convergence of QΩ and ζ_sync as σ increases.67	•	Profile-specific tuning: compare AI vs Aerospace vs Energy with identical σ to see weighting effects.68 69For deeper collaboration (e.g., xAI / RobustBench / GLUE-style testing), this panel can be extended with dataset hooks and richer logging while keeping internal parameters sealed under RFT-IPURL.70 71⸻72 73Rights & Contact74 75All Rights Reserved under RFT-IPURL v1.0 and the Berne Convention (UK Copyright Law).76Author / Contact: Liam Grinstead — liamgrinstead2@gmail.com77